Vectorized multi-site coding for nearest-neighbour neural networks

Abstract : Ising spin neural networks with clipped synapses ± 1 only and with local connectivity are simulated using multi-site coding algorithms. Speeds of over 200 neuron updates per microsecond are achieved by vectorization of the algorithm on the Cray-XMP. Results are presented for two-dimensional networks of up to 512 x 512 neurons. The networks are shown to function as associative memories and the amount of information stored compared to the amount used to store it improves upon fully-connected models.